9 citations · 24 across the 3 of their papers we have counts for
5 papers
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste +4
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determin…
Robustness to Pruning Predicts Generalization in Deep Neural Networks
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez +2
Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural network…
Noise Regularization for Conditional Density Estimation
Jonas Rothfuss, Fabio Ferreira, Simon Boehm +4
Modelling statistical relationships beyond the conditional mean is crucial in many settings. Conditional density estimation (CDE) aims to learn the full conditional probability den…
Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks
Jonas Rothfuss, Fabio Ferreira, Simon Walther +1
Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable and a dependent varia…
Model-Based Reinforcement Learning via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman +3
Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-wor…